Because of the desire for model explanations and the widespread popularity of deep neural networks in the domain of image classification, many feature attribution methods have been proposed in recent years. These methods can roughly be divided into backpropagation-based and perturbation-based methods [1]}. Each attribution method creates a different explanation for the same prediction. This has naturally led to the question of explanation quality: which methods work best? This turns out to be a very difficult question, since there is no ground truth available in the form of “perfect” feature attribution scores.
